In [5]:
import cv2
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
%matplotlib inline
from moviepy.editor import VideoFileClip
In [6]:
# Used the Calibration image from the repository
img = mpimg.imread('camera_cal/calibration2.jpg')
org_img = img
plt.imshow(img)
Out[6]:
In [7]:
# Initialized the objpts and imgpts for the chess board example
objpts = []
imgpts = []
objp = np.zeros((6*9, 3), np.float32)
objp[:,:2] = np.mgrid[0:9, 0:6].T.reshape(-1, 2)
In [8]:
# Conversion of BGR to Gray scale image, and used CV2 function to identify corners
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, corners = cv2.findChessboardCorners(gray, (9,6),None)
print (ret)
In [9]:
# Used the CV2 drawChessboardCorners to plot the corners
if ret == True:
imgpts.append(corners)
objpts.append(objp)
img - cv2.drawChessboardCorners(img, (8,6), corners, ret)
plt.imshow(img)
In [10]:
# Used the CV2 calibrateCamera to calibrate using the Chessboard image with the corners
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpts, imgpts, gray.shape[::-1], None, None)
In [11]:
# Function to get the undistort image with the give Chess board corner image
undist = cv2.undistort(img, mtx, dist, None, mtx)
plt.imshow(undist)
Out[11]:
In [12]:
# Just a show, to display both the original and undistorted image with corners plotted
org_img = mpimg.imread('camera_cal/calibration2.jpg')
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(org_img)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(undist, cmap='gray')
ax2.set_title('Undistorted with Corners', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [13]:
# Function to get the Perspective image with the given distorted image
def corners_unwarp_chess(img, nx, ny, mtx, dist):
# Pass in your image into this function
# Write code to do the following steps
# 1) Undistort using mtx and dist
# 2) Convert to grayscale
# 3) Find the chessboard corners
# 4) If corners found:
# a) draw corners
# b) define 4 source points src = np.float32([[,],[,],[,],[,]])
#Note: you could pick any four of the detected corners
# as long as those four corners define a rectangle
#One especially smart way to do this would be to use four well-chosen
# corners that were automatically detected during the undistortion steps
#We recommend using the automatic detection of corners in your code
# c) define 4 destination points dst = np.float32([[,],[,],[,],[,]])
# d) use cv2.getPerspectiveTransform() to get M, the transform matrix
# e) use cv2.warpPerspective() to warp your image to a top-down view
#delete the next two lines
# Use the OpenCV undistort() function to remove distortion
undist = cv2.undistort(img, mtx, dist, None, mtx)
plt.imshow(undist)
# Convert undistorted image to grayscale
gray = cv2.cvtColor(undist, cv2.COLOR_BGR2GRAY)
#plt.imshow(gray)
# Search for corners in the grayscaled image
ret, corners = cv2.findChessboardCorners(gray, (nx, ny), None)
print (ret)
if ret == True:
# If we found corners, draw them! (just for fun)
cv2.drawChessboardCorners(undist, (nx, ny), corners, ret)
# Choose offset from image corners to plot detected corners
# This should be chosen to present the result at the proper aspect ratio
# My choice of 100 pixels is not exact, but close enough for our purpose here
offset = 100 # offset for dst points
# Grab the image shape
img_size = (gray.shape[1], gray.shape[0])
# For source points I'm grabbing the outer four detected corners
src = np.float32([corners[0], corners[nx-1], corners[-1], corners[-nx]])
# For destination points, I'm arbitrarily choosing some points to be
# a nice fit for displaying our warped result
# again, not exact, but close enough for our purposes
dst = np.float32([[offset, offset], [img_size[0]-offset, offset],
[img_size[0]-offset, img_size[1]-offset],
[offset, img_size[1]-offset]])
# Given src and dst points, calculate the perspective transform matrix
M = cv2.getPerspectiveTransform(src, dst)
# Warp the image using OpenCV warpPerspective()
warped = cv2.warpPerspective(undist, M, img_size)
return warped, M
return
In [14]:
img = mpimg.imread('camera_cal/calibration2.jpg')
plt.imshow(img)
Out[14]:
In [15]:
# Used the corners unwarp function to get the perpsective image
nx = 9 # the number of inside corners in x
ny = 6 # the number of inside corners in y
warped2, M = corners_unwarp_chess(img, nx, ny, mtx, dist)
In [16]:
# Just a plot to show both the original and undistorted Perspective image
org_img = mpimg.imread('camera_cal/calibration2.jpg')
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(org_img)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(warped2, cmap='gray')
ax2.set_title('Undistorted Perspective Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [17]:
# This function original image to undistorted and perspective image
def corners_unwarp(img, mtx, dist):
img_shape = img.shape
print("Image shape:", img_shape)
# Use the OpenCV undistort() function to remove distortion
undist = cv2.undistort(img, mtx, dist, None, mtx)
plt.imshow(undist)
# Convert undistorted image to grayscale
gray = cv2.cvtColor(undist, cv2.COLOR_BGR2GRAY)
plt.imshow(gray)
# Choose offset from image corners to plot detected corners
# This should be chosen to present the result at the proper aspect ratio
# My choice of 100 pixels is not exact, but close enough for our purpose here
offset = 100 # offset for dst points
# Grab the image shape
img_size = (gray.shape[1], gray.shape[0])
#corners = [[580, 460],[710,460],[1150,720],[150,720]]
#corners = [[580, 460],[750,460],[1150,720],[150,720]]
corners = [[220,720], [1110, 720], [722, 470], [570, 470]]
corners_dst = [[320,720], [920, 720], [920, 1], [320, 1]]
# For source points I'm grabbing the outer four detected corners
src = np.float32(corners)
# For destination points, I'm arbitrarily choosing some points to be
# a nice fit for displaying our warped result
# again, not exact, but close enough for our purposes
dst = np.float32([[offset, offset], [img_size[0]-offset, offset],
[img_size[0]-offset, img_size[1]-0],
[offset, img_size[1]-0]])
src = np.float32(corners)
dst = np.float32(corners_dst)
# Given src and dst points, calculate the perspective transform matrix
M = cv2.getPerspectiveTransform(src, dst)
MInv = cv2.getPerspectiveTransform(dst, src)
# Warp the image using OpenCV warpPerspective()
warped = cv2.warpPerspective(undist, M, img_size)
return warped, M, MInv, undist
In [18]:
img = mpimg.imread('test_images/test1.jpg')
plt.imshow(img)
Out[18]:
In [19]:
# Called the corners_unwarp to get the Perspective image
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpts, imgpts, gray.shape[::-1], None, None)
warped2, M, Minv, undist = corners_unwarp(img, mtx, dist)
plt.imshow(warped2)
Out[19]:
In [20]:
# Just a plot to show the Original Image and Undistorted Image
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(undist, cmap='gray')
ax2.set_title('Undistorted Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [24]:
def abs_sobel_thresh(img, orient='x', sobel_kernel=3, thresh=(0, 255)):
# Apply the following steps to img
# 1) Convert to grayscale
# 2) Take the derivative in x or y given orient = 'x' or 'y'
# 3) Take the absolute value of the derivative or gradient
# 4) Scale to 8-bit (0 - 255) then convert to type = np.uint8
# 5) Create a mask of 1's where the scaled gradient magnitude
# is > thresh_min and < thresh_max
# 6) Return this mask as your binary_output image
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Apply x or y gradient with the OpenCV Sobel() function
# and take the absolute value
if orient == 'x':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel))
if orient == 'y':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel))
# Rescale back to 8 bit integer
scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
# Create a copy and apply the threshold
binary_output = np.zeros_like(scaled_sobel)
# Here I'm using inclusive (>=, <=) thresholds, but exclusive is ok too
binary_output[(scaled_sobel >= thresh[0]) & (scaled_sobel <= thresh[1])] = 1
return binary_output
In [26]:
def mag_thresh(img, sobel_kernel=3, mag_thresh=(0, 255)):
# Apply the following steps to img
# 1) Convert to grayscale
# 2) Take the gradient in x and y separately
# 3) Calculate the magnitude
# 4) Scale to 8-bit (0 - 255) and convert to type = np.uint8
# 5) Create a binary mask where mag thresholds are met
# 6) Return this mask as your binary_output image
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Take both Sobel x and y gradients
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# Calculate the gradient magnitude
gradmag = np.sqrt(sobelx**2 + sobely**2)
# Rescale to 8 bit
scale_factor = np.max(gradmag)/255
gradmag = (gradmag/scale_factor).astype(np.uint8)
# Create a binary image of ones where threshold is met, zeros otherwise
binary_output = np.zeros_like(gradmag)
binary_output[(gradmag >= mag_thresh[0]) & (gradmag <= mag_thresh[1])] = 1
return binary_output
In [27]:
# Run the function
mag_binary = mag_thresh(img, sobel_kernel=3, mag_thresh=(40, 200))
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(mag_binary, cmap='gray')
ax2.set_title('Thresholded Magnitude', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [28]:
# Define a function that applies Sobel x and y,
# then computes the direction of the gradient
# and applies a threshold.
def dir_threshold(img, sobel_kernel=3, thresh=(0, np.pi/2)):
# Apply the following steps to img
# 1) Convert to grayscale
# 2) Take the gradient in x and y separately
# 3) Take the absolute value of the x and y gradients
# 4) Use np.arctan2(abs_sobely, abs_sobelx) to calculate the direction of the gradient
# 5) Create a binary mask where direction thresholds are met
# 6) Return this mask as your binary_output image
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Calculate the x and y gradients
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# Take the absolute value of the gradient direction,
# apply a threshold, and create a binary image result
absgraddir = np.arctan2(np.absolute(sobely), np.absolute(sobelx))
binary_output = np.zeros_like(absgraddir)
binary_output[(absgraddir >= thresh[0]) & (absgraddir <= thresh[1])] = 1
return binary_output
In [29]:
# Run the function to get direction threshold
dir_binary = dir_threshold(img, sobel_kernel=15, thresh=(0.7, 1.3))
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(dir_binary, cmap='gray')
ax2.set_title('Thresholded Grad. Dir.', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [30]:
# Choose a Sobel kernel size
ksize = 3 # Choose a larger odd number to smooth gradient measurements
# Apply each of the thresholding functions
gradx = abs_sobel_thresh(img, orient='x', sobel_kernel=ksize, thresh=(10, 200))
grady = abs_sobel_thresh(img, orient='y', sobel_kernel=ksize, thresh=(10, 200))
mag_binary = mag_thresh(img, sobel_kernel=ksize, mag_thresh=(40, 200))
dir_binary = dir_threshold(img, sobel_kernel=ksize, thresh=(0, np.pi/2))
In [31]:
# Gets the combined image
combined = np.zeros_like(dir_binary)
combined[((gradx == 1) & (grady == 1)) | ((mag_binary == 1) & (dir_binary == 1))] = 1
In [32]:
# Plot to show both the original image, and Combined gradiant image
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(combined, cmap='gray')
ax2.set_title('Combined Grad. Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [33]:
# To show the HLS usage with S channel, and plotted both original and combined image with S channel
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
s = hls[:,:,2]
s_binary = np.zeros_like(combined)
s_binary[(s > 170) & (s < 255)] = 1
color_binary = np.zeros_like(combined)
color_binary[(s_binary > 0) | (combined > 0)] = 1
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(color_binary, cmap='gray')
ax2.set_title('S channel + Combined Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [34]:
# Tested the historgram
import numpy as np
histogram = np.sum(color_binary[color_binary.shape[0]//2:,:], axis=0)
plt.plot(histogram)
Out[34]:
In [35]:
warped2, M, Minv, undist = corners_unwarp(img, mtx, dist)
plt.imshow(warped2)
Out[35]:
In [36]:
import numpy as np
histogram = np.sum(warped2[warped2.shape[0]//2:,:], axis=0)
# plt.plot(histogram)
Out[36]:
In [40]:
# Main pipline code that does the Undistort, threshold, HLS/S-channel
def pipeline3(img):
ksize = 3 # Choose a larger odd number to smooth gradient measurements
#img = cv2.resize(img, (720, 405))
#img = cv2.GaussianBlur(img, (ksize, ksize), 0)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpts, imgpts, gray.shape[::-1], None, None)
warped, M, Minv, undist = corners_unwarp(img, mtx, dist)
hls = cv2.cvtColor(warped, cv2.COLOR_RGB2HLS)
s = hls[:,:,2]
#plt.imshow(img)
ksize = 3
# Apply each of the thresholding functions
gradx = abs_sobel_thresh(warped, orient='x', sobel_kernel=ksize, thresh=(10, 200))
grady = abs_sobel_thresh(warped, orient='y', sobel_kernel=ksize, thresh=(10, 200))
mag_binary = mag_thresh(warped, sobel_kernel=ksize, mag_thresh=(10, 200))
#plt.imshow(mag_binary)
dir_binary = dir_threshold(warped, sobel_kernel=ksize, thresh=(0, np.pi/2))
combined = np.zeros_like(dir_binary)
combined[((gradx == 1) & (grady == 1)) | ((mag_binary == 1) & (dir_binary == 1))] = 1
s_binary = np.zeros_like(combined)
s_binary[(s > 170) & (s < 255)] = 1
color_binary = np.zeros_like(combined)
color_binary[(s_binary > 0) | (combined > 0)] = 1
return warped, M, Minv, color_binary, undist
In [ ]:
# Used to detect the lane curve in detect lanes
def lanecurvature(leftx, lefty, rightx, righty) :
# Fit new polynomials to x,y in world space
left_fit_cr = np.polyfit(ploty*ym_per_pix, leftx*xm_per_pix, 2)
right_fit_cr = np.polyfit(ploty*ym_per_pix, rightx*xm_per_pix, 2)
# Calculate the new radii of curvature
left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
# Now our radius of curvature is in meters
print(left_curverad, 'm', right_curverad, 'm')
# Example values: 632.1 m 626.2 m
return left_curverad, right_curverad
In [68]:
# Lane fit function that does the histogram, sliding window, curvature fitting
def lanefit(binary_warped, img):
# Assuming you have created a warped binary image called "binary_warped"
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]/2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# Choose the number of sliding windows
nwindows = 9
# Set height of windows
window_height = np.int(binary_warped.shape[0]/nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated for each window
leftx_current = leftx_base
rightx_current = rightx_base
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2)
cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2)
# Identify the nonzero pixels in x and y within the window
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
# Define conversions in x and y from pixels space to meters
ym_per_pix = 30/720 # meters per pixel in y dimension
xm_per_pix = 3.7/700 # meters per pixel in x dimension
# Fit new polynomials to x,y in world space
#left_curverad, right_curverad = lanecurvature(leftx, lefty, rightx, righty)
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
return ploty, left_fitx, right_fitx, out_img
In [69]:
# final Image does takes the threshold perspective image, lane fitting and
# generates the final image with inverse perspective transform after fitting the green polygon
def finalImage(warped, img, ploty, left_fitx, right_fitx, Minv):
warp_zero = np.zeros_like(warped).astype(np.uint8)
#plt.imshow(warped)
warp_zero = np.zeros_like(warped).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, Minv, (img.shape[1], img.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(img, 1.0, newwarp, 0.6, 0)
return result
In [70]:
img = mpimg.imread('test_images/straight_lines1.jpg')
#plt.imshow(img)
warped, M, Minv, binary_warped, undist = pipeline3(img)
#plt.imshow(color_binary)
In [71]:
plt.imshow(binary_warped, cmap='gray')
Out[71]:
In [72]:
# Original image and S channel combined with Perspective view
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(warped)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(binary_warped, cmap='gray')
ax2.set_title('S channel + Combined Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [73]:
# Histogram showing the peak points in the lane
hist = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
plt.plot(hist)
Out[73]:
In [74]:
# Test perspective image
warped2, M, Minv, undist = corners_unwarp(img, mtx, dist)
plt.imshow(warped2)
Out[74]:
In [75]:
# Test lanefit
ploty, left_fitx, right_fitx, out_img = lanefit(binary_warped, img)
In [76]:
# Test final Image
result = finalImage(binary_warped, img, ploty, left_fitx, right_fitx, Minv)
plt.imshow(result)
Out[76]:
In [77]:
# This is main function, that gets the original image and process
# Pipeline, lanefit and final processing
def process_image(img):
warped, M, Minv, binary_warped, undist = pipeline3(img)
ploty, left_fitx, right_fitx, out_img = lanefit(binary_warped, img)
result = finalImage(binary_warped, img, ploty, left_fitx, right_fitx, Minv)
return result
In [78]:
img2 = mpimg.imread('test_images/test1.jpg')
result2 = process_image(img2)
plt.imshow(result2)
Out[78]:
In [79]:
# Showing the original image and Final Image image for a test image
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img2)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(result2, cmap='gray')
ax2.set_title('Final Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [80]:
# Function to read the video file from repository
# Generate the pipeline video file
clip = VideoFileClip("project_video.mp4")
video_out = "project_video_out.mp4"
video_cap = clip.fl_image(process_image)
%time video_cap.write_videofile(video_out, audio=False)
